关键词: 特征分类, 径向基神经网络, 多域特征融合, 地震动信号

Abstract: A method is proposed to detect,identify and warn the excavation and destruction of underground infrastructure in real time,by feature extraction and classification of the vibratory signal on ground.An improved radial basis neural network classification model is constructed by extracting the multi-domain energy joint distribution feature vector of the wavelet packet transform domain and the ensemble empirical mode transform domain,which adopts machine learning methods to extract stable multi-domain fusion features of the target signal and achieve accurate feature classification prediction.It can be concluded from the simulation experiment results of multi-category ground vibratory signals that the algorithm and model proposed in this paper can effectively improve the classification accuracy of the ground vibratory signals and have strong robustness to ground vibratory interference signals.

Key words: Feature classification, Radial basis neural network, Multi-domain feature fusion, Ground vibratory signal